CU-TMP: Temporal relation classification using syntactic and semantic features

Steven Bethard, James H. Martin

Research output: Contribution to conferencePaperpeer-review

45 Scopus citations

Abstract

We approached the temporal relation identification tasks of TempEval 2007 as pair-wise classification tasks. We introduced a variety of syntactically and semantically motivated features, including temporal-logicbased features derived from running our Task B system on the Task A and C data. We trained support vector machine models and achieved the second highest accuracies on the tasks: 61% on Task A, 75% on Task B and 54% on Task C.

Original languageEnglish (US)
Pages129-132
Number of pages4
StatePublished - 2007
Externally publishedYes
Event4th International Workshop on Semantic Evaluations, SemEval 2007 - Prague, Czech Republic
Duration: Jun 23 2007Jun 24 2007

Other

Other4th International Workshop on Semantic Evaluations, SemEval 2007
Country/TerritoryCzech Republic
CityPrague
Period6/23/076/24/07

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Fingerprint

Dive into the research topics of 'CU-TMP: Temporal relation classification using syntactic and semantic features'. Together they form a unique fingerprint.

Cite this